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Method for delineation of tissue lesions

A biological tissue and image technology, applied in the use of computer program products and corresponding methods, computer-aided delineation of brain tissue damage in stroke patients, system field, can solve problems such as unfastness, unreliability, tissue damage, etc., and achieve improvement Speed ​​and/or reproducibility, effect of minimizing user intervention

Active Publication Date: 2013-10-02
AARHUS UNIV +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The object of the present invention is to provide a method for computer-aided delineation of tissue damage, which solves the problems of slowness and unreliability of the above-mentioned methods in the prior art

Method used

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  • Method for delineation of tissue lesions
  • Method for delineation of tissue lesions
  • Method for delineation of tissue lesions

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0148] Example 1 - Segmentation of an image including TTP data

[0149] Materials and Methods

[0150] Morphological grayscale reconstruction

[0151] Finding a voxel within the perfusion lesion, the so-called seed point, is easily identifiable by eye; however, searching for the maximum value of the image intensity often results in false positive seed points, such as high-intensity points in image regions corresponding to unrelated regions, e.g. eyes, cerebrospinal fluid (CSF), extracerebral strength, etc. To automatically detect seed points, we used morphological grayscale reconstruction to truncate small spikes while preserving the edges of TTP lesions and homogenize the background of TTP images. Compared to conventional smoothing, e.g. using a Gaussian kernel, morphological grayscale reconstruction is less sensitive to spikes and is more suitable for seed point detection for this reason. In the present context, a spike is understood as a relatively small area compared to...

example 2

[0183] Example 2 - Segmentation of images of type MTT data

[0184] Materials and methods

[0185] level set

[0186] This method emerges from the assumption that the mean MTT value, M hypo , in areas of hypoperfusion, compared to the mean M of normal MTT values norm higher. In areas of hypoperfusion, MTT values ​​in M hypo varies around , while in normal tissues they are close to M norm . This corresponds to a low squared error between the image MTT values, MTT(x,y), and the respective mean values. Identifying damage, such as ischemic damage, can then be formulated as finding a smooth, closed curve C that minimizes the total variation (equation (1))

[0187] E=∫ Inside C (MTT(x,y)-M hypo ) 2 dxdy+∫ Outside C (MTT(x,y)-M norm ) 2 dxdy

[0188] It is important that C can represent multiple curves since ischemic regions are not necessarily contiguous, such as in the case of occlusion causing damage in the watershed region. This can be guaranteed by implicitly defi...

example 3

[0223] Example 3 - Segmentation of an image obtained by DWI

[0224] Materials and Methods

[0225] Morphological grayscale reconstruction

[0226] DWI lesions are readily recognized by the eye as areas of hyperintensity, although diffuse lesions contain artifactual hyperintensity; and due to body structure and noise artifacts, the hyperintensity spreads throughout the image. Therefore, simple thresholding of image intensities leads to high false positive and false negative rates, as Figure 11B shown. Typical image blurring, e.g. by convolution with a Gaussian kernel, is somewhat remedied to the artifact, although at the cost of potentially "moving" the lesion boundary. Morphological grayscale reconstruction uniquely truncates peaks in the image, tending to preserve the original boundaries of cohesive regions, as determined by the kernel size. We used morphological grayscale reconstruction to enhance the contrast between DWI lesions and the background. Morphological gray...

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Abstract

The present invention relates to a method for estimating a measure of a penumbra size of a penumbra in biological tissue wherein first and second images obtained respectively by perfusion weighted imaging (PWI) and diffusion weighted imaging (DWI) are analyzed, and wherein the analysis of the first image includes application of a level set method, and the analysis of the second image includes application of a grayscale morphological operation. In a further embodiment of the invention, a connected component labeling algorithm maybe applied on any one of the first and second image. The invention further relates to a system, a computer program product and use of a corresponding method.

Description

technical field [0001] The invention relates to a method for computer-aided drawing of tissue damage, in particular to a method for computer-aided drawing of brain tissue damage of stroke patients. The invention further relates to the use of the system, the computer program product and the corresponding method. Background technique [0002] In the case of an acute stroke in a patient, the reduction in blood supply causes a part of the brain tissue to suffer ischemia, for example in patients suffering from insufficient supply of oxygen and nutrients, this part is called perfusion injury. A subfraction of perfusion injury is irreversible injury, which is referred to as core injury or diffuse injury. The remainder of the perfusion injury, the so-called ischemic penumbra, can be salvaged if the blood supply is restored within a short period of time, eg within a few hours. Identifying the volume and location of the penumbra is critical in determining the treatment strategy for ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
CPCG06T7/0012G06T2207/10088G06T2207/20036G06T2207/30016G06T7/149G06T7/155G06T1/00G06T7/00G06T11/003
Inventor 卡瑟班·那亘提拉亚基姆·莫里德森拉尔斯·李斯加德·里伯
Owner AARHUS UNIV
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